It is known that human emotional states have underlying physiological correlates reflecting activity of the autonomic nervous system. A variety of physiological signals have been used to detect emotional states. However, it is not easy to use physiological data to monitor emotions accurately because physiological signals are susceptible to artifact, particularly with mobile users, and the relationship between physiological measures and positive or negative emotional states is not straightforward.
A standard model separates emotional states into two axes: arousal (e.g. calm-excited) and valence (negative-positive]. Thus emotions can be broadly categorized into high arousal states, such as fear/anger/frustration (negative valence) and joy/excitement/elation (positive valence); or low arousal states, such as depressed/sad/bored (negative valence) and relaxed/peaceful/blissful (positive valence).